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Retrieval-augmented generation for generative artificial intelligence in health care
Rui Yang1, Yilin Ning1, Emilia Keppo2
1Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Abstract:
Generative artificial intelligence has brought disruptive innovations in health care but faces certain challenges. Retrieval-augmented generation (RAG) enables models to generate more reliable content by leveraging the retrieval of external knowledge. In this perspective, we analyze the possible contributions that RAG could bring to health care in equity, reliability, and personalization. Additionally, we discuss the current limitations and challenges of implementing RAG in medical scenarios.
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